U.S. patent application number 10/936339 was filed with the patent office on 2005-07-07 for apparatus and methods of cortical surface registration and deformation tracking for patient-to-image alignment in relation to image-guided surgery.
This patent application is currently assigned to Vanderbilt University. Invention is credited to Dawant, Benoit M., Miga, Michael I., Sinha, Tuhin K..
Application Number | 20050148859 10/936339 |
Document ID | / |
Family ID | 36036665 |
Filed Date | 2005-07-07 |
United States Patent
Application |
20050148859 |
Kind Code |
A1 |
Miga, Michael I. ; et
al. |
July 7, 2005 |
Apparatus and methods of cortical surface registration and
deformation tracking for patient-to-image alignment in relation to
image-guided surgery
Abstract
A cortical surface registration procedure related to a
diagnostic or surgical procedure. In one embodiment, the procedure
includes the steps of pre-operatively obtaining a first textured
point cloud of the cortical surface of a targeted region of a brain
of a living subject, intra-operatively obtaining optically a second
textured point cloud of the cortical surface of the brain of the
living subject, and aligning the first textured point cloud of the
cortical surface to the second textured point cloud of the cortical
surface so as to register images of the brain of the living subject
to the cortical surface of the living subject.
Inventors: |
Miga, Michael I.; (Franklin,
TN) ; Dawant, Benoit M.; (Nashville, TN) ;
Sinha, Tuhin K.; (Nashville, TN) |
Correspondence
Address: |
MORRIS MANNING & MARTIN LLP
1600 ATLANTA FINANCIAL CENTER
3343 PEACHTREE ROAD, NE
ATLANTA
GA
30326-1044
US
|
Assignee: |
Vanderbilt University
Nashville
TN
|
Family ID: |
36036665 |
Appl. No.: |
10/936339 |
Filed: |
September 8, 2004 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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60501514 |
Sep 8, 2003 |
|
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Current U.S.
Class: |
600/410 |
Current CPC
Class: |
A61B 2017/00694
20130101; A61B 34/10 20160201; A61B 2034/256 20160201; A61B 34/20
20160201; A61B 2090/378 20160201; A61B 2090/373 20160201; G06T 7/38
20170101; G06K 2209/051 20130101; A61B 90/36 20160201; A61B
2034/107 20160201; G06K 9/6211 20130101; A61B 2034/105 20160201;
A61B 2034/2055 20160201; G06K 9/6212 20130101; A61B 2090/364
20160201; G06K 9/00214 20130101; G06T 7/35 20170101; A61B 2034/2065
20160201 |
Class at
Publication: |
600/410 |
International
Class: |
A61B 005/05 |
Goverment Interests
[0003] This invention was made in part with U.S. Government support
under Grant NIH/NCI IR21 CA89657-01A2, awarded by the National
Institute of Health. The U.S. Government may have certain rights in
this invention.
Claims
What is claimed is:
1. A method of registering an image volume of a brain of a living
subject to a cortical surface of at least one targeted region of
the brain of a living subject, comprising the steps of: a.
pre-operatively acquiring the image volume from the brain of the
living subject; b. generating a grayscale encoded brain surface
from the acquired image volume; c. intra-operatively obtaining a
textured point cloud of the cortical surface of the at least one
targeted region of the brain of the living subject; and d. aligning
the grayscale-encoded brain surface to the textured point cloud of
the cortical surface so as to register the image volume of the
brain with respect to the cortical surface of the at least one
targeted region of the brain.
2. The method of claim 1, wherein the image volume of the brain of
the living subject comprises image data with respect to the brain
surface geometry.
3. The method of claim 2, wherein the image data with respect to
the brain surface geometry is obtained through the use of at least
one of positron emission tomography, electroencephalography,
computer tomography, functional magnetic resonance imaging and
magnetic resonance imaging.
4. The method of claim 1, wherein the step of generating a
grayscale encoded brain surface comprises the steps of: a.
segmenting the acquired image volume of the brain of the living
subject; b. extracting a point cloud representation of the brain
surface geometry from the segmented image volume; and c. performing
a ray-casting and voxel intensity averaging on the point cloud
representation so as to generate a grayscale encoded brain surface
that contains intensity patterns representing sulcal-gyrus
differences and contrast-enhanced vasculature.
5. The method of claim 1, wherein the step of obtaining a textured
point cloud of the cortical surface is performed with an optical
device that is capable of obtaining frequency, intensity and
geometric data with respect to the cortical surface
simultaneously.
6. The method of claim 5, wherein the optical device is a laser
range scanner.
7. The method of claim 6, wherein the step of intra-operatively
obtaining a textured point cloud of the cortical surface comprises
the steps of: a. optically scanning an exposed brain surface of the
living subject during surgery with the laser range scanner; b.
capturing surface-reflected light from the brain surface of the
living subject; c. acquiring a point cloud representation of the
geometry of the cortical surface from the captured
surface-reflected light; and d. color-encoding the acquired point
cloud representation with intensity values of a field of view so as
to obtain a textured point cloud of the cortical surface of the at
least one targeted region of the brain.
8. The method of claim 1, wherein the step of aligning the
grayscale-encoded brain surface to the textured point cloud of the
cortical surface comprises the steps of: a. registering the
grayscale-encoded brain surface of the brain to the textured point
cloud of the cortical surface of the targeted region of the brain
using an iterative closest point algorithm; and b. optimizing the
grayscale-encoded brain surface of the brain to the textured point
cloud of the cortical surface of the targeted region of the brain
using normalized mutual information.
9. The method of claim 8, wherein the registering step comprises
the steps of: a. pairing corresponding points from the
grayscale-encoded brain surface of the brain and the textured point
cloud of the cortical surface of the targeted region of the brain
according to a closest distance metric; b. executing a point-based
registration; c. updating the closest distance metric accordingly;
and d. repeating steps (a)-(c) until a disparity function d
satisfies a specified tolerance, wherein the disparity function d
has the form of: 5 d = 1 N j N ; y j - T ( x j ) r; 2 wherein
T(x.sub.j) represents a rigid transformation of N points on a
source surface to corresponding points on a target surface,
y.sub.j.
10. The method of claim 9, wherein the optimizing step comprises
the steps of: a. choosing a normalized mutual information in the
form of 6 NMI ( x , y ) = H ( x ) + H ( y ) H ( x , y ) wherein
H(x) and H(x, y) are the marginal and joint entropies of the point
clouds, respectively; b. using the closest distance metric to
determine proper intensity correspondence among a source surface
and a target surface; c. fitting a spherical geometry to reduce the
registration degrees of freedom; and d. optimizing the normalized
mutual information using an iterative procedure.
11. A cortical surface registration procedure related to a
diagnostic or surgical procedure, comprising: a. pre-operatively
obtaining a first textured point cloud of the cortical surface of a
targeted region of a brain of a living subject; b.
intra-operatively obtaining optically a second textured point cloud
of the cortical surface of the brain of the living subject; and c.
aligning the first textured point cloud of the cortical surface to
the second textured point cloud of the cortical surface so as to
register images of the brain of the living subject to the cortical
surface of the living subject.
12. The procedure of claim 11, wherein the step of pre-operatively
obtaining a first textured point cloud comprises the steps of: a.
pre-operatively acquiring an image volume from the brain of the
living subject; b. segmenting the acquired image volume; c.
extracting a point cloud representation of the brain surface
geometry from the segmented image volume; d. performing a
ray-casting and voxel intensity averaging on the point cloud
representation so as to generate a grayscale encoded brain surface
that contains intensity patterns representing sulcal-gyrus
differences and contrast-enhanced vasculature; and e. obtaining the
first point cloud from the grayscale encoded brain surface.
13. The procedure of claim 12, wherein the image volume of the
brain of the living subject comprises image data with respect to
the brain surface geometry.
14. The procedure of claim 13, wherein the image data with respect
to the brain surface geometry is obtained through the use of at
least one of positron emission tomography, electroencephalography,
computer tomography, functional magnetic resonance imaging and
magnetic resonance imaging.
15. The procedure of claim 11, wherein the step of
intra-operatively obtaining optically a second textured point cloud
comprises the steps of: a. optically scanning an exposed brain
surface of the living subject during surgery; b. capturing
surface-reflected light from the brain surface of the living
subject; c. acquiring a point cloud representation of the geometry
of the cortical surface from the captured surface-reflected light;
and d. color-encoding the acquired point cloud representation with
intensity values of a field of view so as to obtain the second
textured point cloud of the cortical surface of the at least one
targeted region of the brain.
16. The procedure of claim 15, wherein the step of optically
scanning an exposed brain surface is performed with an optical
device that is capable of obtaining frequency, intensity and
geometric data with respect to the cortical surface
simultaneously.
17. The procedure of claim 16, wherein the optical device is a
laser range scanner.
18. The procedure of claim 11, wherein the step of aligning the
first textured point cloud of the cortical surface to the second
textured point cloud of the cortical surface comprises the steps
of: a. registering the first textured point cloud of the cortical
surface to the second textured point cloud of the cortical surface
using an iterative closest point algorithm; and b. optimizing the
first textured point cloud of the cortical surface to the second
textured point cloud of the cortical surface using normalized
mutual information.
19. A system for cortical surface registration related to a
diagnostic or surgical procedure, comprising: a. an imaging
acquiring device for pre-operatively obtaining a first textured
point cloud of the cortical surface of a targeted region of a brain
of a living subject; b. an optical device for intra-operatively
obtaining a second textured point cloud of the cortical surface of
the brain of the living subject; and c. a computer for receiving
and processing data related to the first textured point cloud of
the cortical surface and the second textured point cloud of the
cortical surface so as to register images of the brain of the
living subject to the cortical surface of the living subject.
20. The system of claim 19, further comprising a display device
coupled to the computer for displaying the cortical surface
registration dynamically to facilitate the diagnostic or surgical
procedure.
21. The system of claim 19, wherein the imaging acquiring device
comprises at least one of positron emission tomography device,
electroencephalography device, computer tomography device,
functional magnetic resonance imaging device and magnetic resonance
imaging device.
22. The system of claim 19, wherein the optical device comprises a
laser device.
23. The system of claim 22, wherein the laser device is a laser
range scanner adapted for optically scanning an exposed brain
surface of the living subject during the diagnostic or surgical
procedure.
24. The system of claim 23, wherein the optical device further
comprises a first digital camera adapted for capturing
surface-reflected light from the brain surface of the living
subject when the brain surface of the living subject is scanned by
the laser range scanner.
25. The system of claim 24, wherein the optical device further
comprises a second digital camera adapted for capturing an image of
the surgical field of view.
26. A method of deformable cortical surface registration related to
a diagnostic or surgical procedure to track brain deformation,
comprising the steps of: a. obtaining a first 3D point cloud of a
brain of a living subject prior to or during brain deformation,
wherein each 3D point of the first 3D point cloud is color-encoded;
b. generating a first 2D photographic image from the first 3D point
cloud; c. obtaining a second 3D point cloud of the brain during or
after brain deformation, wherein each 3D point of the second 3D
point cloud representation is color-encoded; d. generating a second
2D photographic image from the second 3D point cloud; and e.
non-rigidly aligning the first 2D photographic image and the second
2D photographic image so as to track the brain deformation.
27. The method of claim 26, wherein the step of obtaining a first
3D point cloud comprises the steps of: a. optically scanning an
exposed brain surface of the living subject at a time prior to or
during brain deformation; b. capturing surface-reflected light from
the brain surface of the living subject; c. acquiring a first point
cloud representation of the geometry of the cortical surface from
the captured surface-reflected light; and d. color-encoding the
acquired each point of the first point cloud representation by a
direct linear transform representation so as to construct the first
3D point cloud.
28. The method of claim 27, wherein the step of optically scanning
an exposed brain surface is performed with an optical device that
is capable of obtaining frequency, intensity and geometric data
with respect to the cortical surface simultaneously.
29. The method of claim 28, wherein the optical device is a laser
range scanner.
30. The method of claim 27, wherein the step of obtaining a second
3D point cloud comprises the steps of: a. optically scanning an
exposed brain surface of the living subject at a time during or
after the step of obtaining a first 3D point cloud; b. capturing
surface-reflected light from the brain surface of the living
subject; c. acquiring a second point cloud representation of the
geometry of the cortical surface from the captured
surface-reflected light; and d. color-encoding the acquired each
point of the second point cloud representation by a direct linear
transform representation so as to construct the second 3D point
cloud.
31. The method of claim 26, wherein the step of non-rigidly
aligning the first 2D photographic image and the second 2D
photographic image so as to track the brain deformation comprises
the steps of: a. transforming the first and second 2D photographic
images from RGB images into corresponding gray level images; and b.
obtaining a final deformation field that registers gray level
images one to the other.
32. The method of claim 31, wherein the step of obtaining a
deformation field comprises the steps of: a. calculating a
deformation field for each of a series of levels, wherein each
level is corresponding to a particular combination of scale and
resolution for an image; and b. adding all the deformation fields
for all of the series of levels to generate the final deformation
field.
33. A system of deformable cortical surface registration related to
a diagnostic or surgical procedure to track brain deformation,
comprising: a. image data acquiring means for obtaining a first 3D
point cloud of a brain of a living subject prior to or during brain
deformation, wherein each 3D point of the first 3D point cloud is
color-encoded, and a second 3D point cloud of the brain during or
after brain deformation, wherein each 3D point of the second 3D
point cloud representation is color-encoded, respectively; b. image
generating means for generating a first 2D photographic image from
the first 3D point cloud, and a second 2D photographic image from
the second 3D point cloud, respectively; and c. registration means
for non-rigidly aligning the first 2D photographic image and the
second 2D photographic image so as to track the brain
deformation.
34. The system of claim 33, wherein the image data acquiring means
is capable of: a. optically scanning an exposed brain surface of
the living subject at a selected time; b. capturing
surface-reflected light from the brain surface of the living
subject; c. acquiring a point cloud representation of the geometry
of the cortical surface from the captured surface-reflected light;
and d. color-encoding the acquired each point of the point cloud
representation by a direct linear transform representation so as to
construct a 3D point cloud.
35. The system of claim 34, wherein the image data acquiring means
comprises an optical device that is capable of obtaining frequency,
intensity and geometric data with respect to the cortical surface
simultaneously.
36. The system of claim 35, wherein the optical device is a laser
range scanner.
37. The system of claim 35, wherein the image data acquiring means
further comprises a first digital camera adapted for capturing
surface-reflected light from the brain surface of the living
subject when the brain surface of the living subject is scanned by
the laser range scanner.
38. The system of claim 37, wherein the image data acquiring means
further comprises a second digital camera adapted for capturing an
image of the surgical field of view.
39. The system of claim 33, wherein the image generating means
comprises a computer.
40. The system of claim 33, wherein the registration means
comprises a controller.
Description
CROSS-REFERENCE TO RELATED PATENT APPLICATION
[0001] This application claims the benefit, pursuant to 35 U.S.C.
.sctn.119(e), of provisional U.S. patent application Ser. No.
60/501,514, filed Sep. 8, 2003, entitled "APPARATUS AND METHODS OF
CORTICAL SURFACE REGISTRATION AND DEFORMATION TRACKING FOR
PATIENT-TO-IMAGE ALIGNMENT DURING IMAGE-GUIDED SURGERY," by Michael
I. Miga, Benoit M. Dawant and Tuhin K. Sinha, which is incorporated
herein by reference in its entirety.
[0002] Some references, which may include patents, patent
applications and various publications, are cited and discussed in
the description of this invention. The citation and/or discussion
of such references is provided merely to clarify the description of
the present invention and is not an admission that any such
reference is "prior art" to the invention described herein. All
references cited and discussed in this specification are
incorporated herein by reference in their entireties and to the
same extent as if each reference was individually incorporated by
reference. In terms of notation, hereinafter, "[n]" represents the
nth reference cited in the reference list. For example, [28]
represents the 28th reference cited in the reference list, namely,
M. I. Miga, K. D. Paulsen, J. M. Lemery, S. D. Eisner, A. Hartov,
F. E. Kennedy, and D. W. Roberts, "Model-updated image guidance:
Initial clinical experiences with gravity-induced brain
deformation," IEEE Trans. Med. Imag., vol. 18, pp. 866-874, October
1999.
FIELD OF THE INVENTION
[0004] The present invention generally relates to image-guided
surgery, and in particular to apparatus and methods of cortical
surface registration and/or deformation tracking for
patient-to-image alignment in relation to image-guided surgery.
BACKGROUND OF THE INVENTION
[0005] Image-guided surgery (hereinafter "IGS") involves a
patient-specific anatomical images pre-operatively acquired that
spatially localizes pathology, digitization technology that allows
the identification and tracking of targeted points of interest in a
patient's physical space in an operating room (hereinafter "OR"),
and alignments of the patient-specific images to the patient's
physical space in the OR such that the digitization technology can
be referenced to the patient-specific images and used for guidance
during surgery. Central to the IGS is the method of registering an
image space (a coordinate system corresponding to the pre-operative
images) to a physical space (a coordinate system corresponding to
the intra-operative anatomy of the patient). Once the registration
is performed, all pre-operative planning and acquired data related
to the patient's anatomy could be displayed intra-operatively to a
surgeon and used for assistance in surgical guidance and
treatment.
[0006] Over the past years, a variety of registration methods have
been developed. Among them, a point-based registration (hereinafter
"PBR") has been mostly characterized and thoroughly examined,
whereby landmarks are localized in patient's image volumes and
aligned with corresponding landmarks digitized in physical space of
the patient intra-operatively. The landmarks, or fiducials, can be
either natural structures such as a nose bridge of the patient, or
synthetic components such as small cylindrical markers adhered to
the skin of the patient or markers implanted into the skull of the
patient prior to image acquisitions [1, 2]. Further analysis of
configurations of fiducial markers, optimum marker numbers, and
effects on target localization error has been reported [2]. The PBR
technique has proven clinically accurate and useful. However,
utilization of the PRG method requires a preliminary surgery for
implantation of the fiducial markers to predetermined positions in
a patient's anatomy.
[0007] Another technique for the registration is accomplished by
identifying two geometric surfaces that are the same in an image
space and a physical space of a patient, respectively, and aligning
them between the two spaces. The ability to acquire surface data
using a probe, such as optical probe, electromagnetic probe, and/or
ultrasound probe, and lasers [3-7] in conjunction with surface
extraction algorithms applied to imaging data has led to new
methods of surface based registrations [8]. The primary difference
between the surface-based registration and the PBR is that the
surface based registration does not require a one-to-one point
correspondence. On the other hand, an averaging effect in the
surface-based registration serves to reduce uncorrelated
localization error generated during the acquisition of spatially
well-resolved surface data. However, the surface based alignment
techniques are limited with facts, for example, scalps lack
geometric specificity, and skin surfaces may deform due to
intra-operative drugs or procedural retraction [9]. An alternative
registration technique, less commonly used for IGS purposes, is an
intensity-based or volume registration approach [2], which is
usually applied for alignments of a source image volume to a target
image volume.
[0008] However, recent studies have shown limitations in accuracy
with current image-guided procedures. The discrepancy observed is a
by-product of the rigid-body assumptions and techniques used during
the registration process. Specifically, with neurosurgery,
registration is provided by markers attached to the skull of a
patient or on the skin surrounding the skull of a patient, where
soft-tissue deformations of the brain during surgery may result in
significant errors in aligning a pre-operative image space to an
actual physical space. One of the earliest observed instances of
the error was reported by Kelly et al. [10]. More recently, Nauta
has measured this shift that is of an order of 5 mm [11].
Subsequent investigations in intra-operative brain surface
movements have shown that an average deformation for brain shifts
is about 1 cm. Moreover, predispositions for brain movement in the
direction of gravity have been investigated [12,13].
[0009] This has lead studies to develop methods and techniques that
can compensate for intra-operative brain shifts. One of the methods
includes the use of conventional imaging modalities during surgery,
i.e. intra-operative computed tomography (hereinafter "iCT"),
intra-operative magnetic resonance (hereinafter "iMR"), and/or
intra-operative ultrasound (hereinafter "iUS") imaging. When
available, intra-operative images are registered to pre-operative
images using a number of nonrigid intra-modal and/or inter-modal
registration methods. In the 1980s, there was a significant effort
to incorporate iCT during surgery as a means for acquiring
intra-operative image series. However, dose considerations of
repeatedly using computed tomography (hereinafter "CT") scanning in
the OR have hindered adoption of the iCT technique [14]. More
recently, several medical centers have explored the use of iMR
imaging for data acquisition and shift compensation [15-18] and
have developed elegant and sophisticated methods for visualization
in the OR [3, 19, 20]. Although conceptually appealing, the
exorbitant cost and cumbersome nature of such a system (e.g., need
for a MR compatible OR) have left their widespread adoption
uncertain. In addition to these logistical concerns, recent reports
have demonstrated potential problems related to surgically induced
contrast enhancement that could be often confused with
contrast-enhancing residual tumor [21], and image distortions from
susceptibility and/or eddy current artifacts related to the
presence of MR compatible Yasargil clips for aneurysm clipping
procedures [22]. An alternative to iCT and iMR imaging is the use
of iUS [23-26], where locally reconstructed iUS image volumes may
provide a real-time guidance feedback. However, the quality of the
iUS images over the course of surgery limits their effectiveness in
shift compensation.
[0010] A possible alternative to high-cost intra-operative imaging
is to use computational methods to compensate for brain shifts in
IGS. A strategy for using computational methods to correct for
brain shifts in neurosurgery was highlighted by Roberts et al.
[27]. Rapidly acquiring minimally invasive data that describes
changes in brain geometry during surgery is necessary to develop a
computational approach that accounts for brain deformations. In
these methods, intra-operative surface data are combined with a
statistical and/or mathematical model of the soft-tissue mechanics
that describe brain deformation [27]. Physical models have been
successfully used to reconstitute 75% to 85% of the shift occurring
under loads similar to a clinical setting. A detailed work
regarding the fidelity of such computations within animal and human
systems has been reported [28, 29]. Registrations of multimodality
images by elastic matching technique have also been studied [30,
31]. Deformable templates for large deformation warping of images
have been utilized [32]. However, the computational methods may not
be able to effectively predict the extent of tumor margins.
[0011] Therefore, a heretofore unaddressed need exists in the art
to address the aforementioned deficiencies and inadequacies.
SUMMARY OF THE INVENTION
[0012] In one aspect, the present invention relates to a method of
registering an image volume of a brain of a living subject to a
cortical surface of at least one targeted region of the brain of
the living subject. In one embodiment, the method includes the step
of pre-operatively acquiring the image volume from the brain of the
living subject, wherein the image volume of the brain of the living
subject comprises image data with respect to the brain surface
geometry. The image data with respect to the brain surface
geometry, in one embodiment, is obtained through the use of at
least one of positron emission tomography, electroencephalography,
computer tomography, functional magnetic resonance imaging and
magnetic resonance imaging.
[0013] The method further includes the step of generating a
grayscale encoded brain surface from the acquired image volume. In
one embodiment, the generating step comprises the steps of
segmenting the acquired image volume of the brain of the living
subject, extracting a point cloud representation of the brain
surface geometry from the segmented image volume, and performing a
ray-casting and voxel intensity averaging on the point cloud
representation so as to generate a grayscale encoded brain surface
that contains intensity patterns representing sulcal-gyrus
differences and contrast-enhanced vasculature.
[0014] Furthermore, the method includes the step of
intra-operatively obtaining a textured point cloud of the cortical
surface of the at least one targeted region of the brain of the
living subject. The step of obtaining a textured point cloud of the
cortical surface is performed with an optical device that is
capable of obtaining frequency, intensity and geometric data with
respect to the cortical surface simultaneously. In one embodiment,
the optical device is a laser range scanner (hereinafter "LRS").
The step of intra-operatively obtaining a textured point cloud of
the cortical surface comprises the steps of optically scanning an
exposed brain surface of the living subject during surgery with the
laser range scanner, capturing surface-reflected light from the
brain surface of the living subject, acquiring a point cloud
representation of the geometry of the cortical surface from the
captured surface-reflected light, and color-encoding the acquired
point cloud representation with intensity values of a field of view
so as to obtain a textured point cloud of the cortical surface of
the at least one targeted region of the brain.
[0015] Moreover, the method includes the step of aligning the
grayscale-encoded brain surface to the textured point cloud of the
cortical surface so as to register the image volume of the brain
with respect to the cortical surface of the at least one targeted
region of the brain. In one embodiment, the step of aligning the
grayscale-encoded brain surface to the textured point cloud of the
cortical surface comprises the steps of registering the
grayscale-encoded brain surface of the brain to the textured point
cloud of the cortical surface of the targeted region of the brain
using an iterative closest point algorithm, and optimizing the
grayscale-encoded brain surface of the brain to the textured point
cloud of the cortical surface of the targeted region of the brain
using normalized mutual information. In one embodiment, the
registering step includes the steps of pairing corresponding points
from the grayscale-encoded brain surface of the brain and the
textured point cloud of the cortical surface of the targeted region
of the brain according to a closest distance metric, executing a
point-based registration, updating the closest distance metric
accordingly, and repeating the pairing step, the executing step and
the updating step until a disparity function d satisfies a
specified tolerance, wherein the disparity function d has the form
of 1 d = 1 N j N ; y j - T ( x j ) r; 2 ,
[0016] where T(x.sub.j) represents a rigid transformation of N
points on a source surface to corresponding points on a target
surface, y.sub.j. The optimizing step, in one embodiment, comprises
the steps of choosing a normalized mutual information in the form
of 2 NMI ( x , y ) = H ( x ) + H ( y ) H ( x , y ) ,
[0017] where H(x) and H(x, y) are the marginal and joint entropies
of the point clouds, respectively, using the closest distance
metric to determine proper intensity correspondence among a source
surface and a target surface, fitting a spherical geometry to
reduce the registration degrees of freedom, and optimizing the
normalized mutual information using an iterative procedure.
[0018] In another aspect, the present invention relates to a
cortical surface registration procedure related to a diagnostic or
surgical procedure. In one embodiment, the cortical surface
registration procedure includes the steps of pre-operatively
obtaining a first textured point cloud of the cortical surface of a
targeted region of a brain of a living subject, intra-operatively
obtaining optically a second textured point cloud of the cortical
surface of the brain of the living subject, and aligning the first
textured point cloud of the cortical surface to the second textured
point cloud of the cortical surface so as to register images of the
brain of the living subject to the cortical surface of the living
subject.
[0019] In one embodiment, the step of pre-operatively obtaining a
first textured point cloud comprises the steps of pre-operatively
acquiring an image volume from the brain of the living subject,
segmenting the acquired image volume, extracting a point cloud
representation of the brain surface geometry from the segmented
image volume, performing a ray-casting and voxel intensity
averaging on the point cloud representation so as to generate a
grayscale encoded brain surface that contains intensity patterns
representing sulcal-gyrus differences and contrast-enhanced
vasculature, and obtaining the first point cloud from the grayscale
encoded brain surface.
[0020] Furthermore, the step of intra-operatively obtaining
optically a second textured point cloud includes the steps of
optically scanning an exposed brain surface of the living subject
during surgery, capturing surface-reflected light from the brain
surface of the living subject, acquiring a point cloud
representation of the geometry of the cortical surface from the
captured surface-reflected light, and color-encoding the acquired
point cloud representation with intensity values of a field of view
so as to obtain the second textured point cloud of the cortical
surface of the at least one targeted region of the brain.
[0021] Additionally, the step of aligning the first textured point
cloud of the cortical surface to the second textured point cloud of
the cortical surface comprises the steps of registering the first
textured point cloud of the cortical surface to the second textured
point cloud of the cortical surface using an iterative closest
point algorithm, and optimizing the first textured point cloud of
the cortical surface to the second textured point cloud of the
cortical surface using normalized mutual information.
[0022] In yet another aspect, the present invention relates to a
system for cortical surface registration related to a diagnostic or
surgical procedure. In one embodiment, the system has an imaging
acquiring device for pre-operatively obtaining a first textured
point cloud of the cortical surface of a targeted region of a brain
of a living subject, an optical device for intra-operatively
obtaining a second textured point cloud of the cortical surface of
the brain of the living subject, and a computer for receiving and
processing data related to the first textured point cloud of the
cortical surface and the second textured point cloud of the
cortical surface so as to register images of the brain of the
living subject to the cortical surface of the living subject. The
system further includes a display device coupled to the computer
for displaying the cortical surface registration dynamically to
facilitate the diagnostic or surgical procedure.
[0023] In one embodiment, the imaging acquiring device includes at
least one of positron emission tomography device,
electroencephalography device, computer tomography device,
functional magnetic resonance imaging device and magnetic resonance
imaging device. The optical device comprises a laser device. In one
embodiment, the laser device is a laser range scanner adapted for
optically scanning an exposed brain surface of the living subject
during the diagnostic or surgical procedure. Furthermore, the
optical device includes a first digital camera adapted for
capturing surface-reflected light from the brain surface of the
living subject when the brain surface of the living subject is
scanned by the laser range scanner. Moreover, the optical device
includes a second digital camera adapted for capturing an image of
the surgical field of view.
[0024] In a further aspect, the present invention relates to a
method of deformable cortical surface registration related to a
diagnostic or surgical procedure to track brain deformation. In one
embodiment, the method includes the steps of obtaining a first
three-dimensional (hereinafter "3D") point cloud of a brain of a
living subject prior to or during brain deformation, where each 3D
point of the first 3D point cloud is color-encoded, generating a
first two-dimensional (hereinafter "2D") photographic image from
the first 3D point cloud, obtaining a second 3D point cloud of the
brain during or after brain deformation, wherein each 3D point of
the second 3D point cloud representation is color-encoded,
generating a second 2D photographic image from the second 3D point
cloud, and non-rigidly aligning the first 2D photographic image and
the second 2D photographic image so as to track the brain
deformation.
[0025] In one embodiment, the step of obtaining a first 3D point
cloud comprises the steps of optically scanning an exposed brain
surface of the living subject at a time prior to or during brain
deformation, capturing surface-reflected light from the brain
surface of the living subject, acquiring a first point cloud
representation of the geometry of the cortical surface from the
captured surface-reflected light, and color-encoding the acquired
each point of the first point cloud representation by a direct
linear transform representation so as to construct the first 3D
point cloud. The step of optically scanning an exposed brain
surface, in one embodiment, is performed with an optical device
that is capable of obtaining frequency, intensity and geometric
data with respect to the cortical surface simultaneously, wherein
the optical device is a laser range scanner.
[0026] The step of obtaining a second 3D point cloud includes the
same steps as obtaining the first 3D point cloud, but the step is
performed during or after the step of obtaining the first 3D point
cloud.
[0027] Additionally, the step of non-rigidly aligning the first 2D
photographic image and the second 2D photographic image so as to
track the brain deformation includes the steps of transforming the
first and second 2D photographic images from RGB images into
corresponding gray level images, and obtaining a final deformation
field that registers gray level images one to the other. In one
embodiment, the step of obtaining a deformation field comprises the
steps of calculating a deformation field for each of a series of
levels, wherein each level is corresponding to a particular
combination of scale and resolution for an image, and adding all
the deformation fields for all of the series of levels to generate
the final deformation field.
[0028] In yet a further aspect, the present invention relates to a
system of deformable cortical surface registration related to a
diagnostic or surgical procedure to track brain deformation. In one
embodiment, the system has image data acquiring means for obtaining
a first 3D point cloud of a brain of a living subject prior to or
during brain deformation, where each 3D point of the first 3D point
cloud is color-encoded, and a second 3D point cloud of the brain
during or after brain deformation, where each 3D point of the
second 3D point cloud representation is color-encoded,
respectively. The image data acquiring means is capable of
optically scanning an exposed brain surface of the living subject
at a selected time, capturing surface-reflected light from the
brain surface of the living subject, acquiring a point cloud
representation of the geometry of the cortical surface from the
captured surface-reflected light, and color-encoding the acquired
each point of the point cloud representation by a direct linear
transform representation so as to construct a 3D point cloud. In
one embodiment, the image data acquiring means includes an optical
device that is capable of obtaining frequency, intensity and
geometric data with respect to the cortical surface simultaneously.
The optical device, in one embodiment, is a laser range scanner.
Furthermore, the image data acquiring means includes a first
digital camera adapted for capturing surface-reflected light from
the brain surface of the living subject when the brain surface of
the living subject is scanned by the laser range scanner.
Additionally, the image data acquiring means comprises a second
digital camera adapted for capturing an image of the surgical field
of view.
[0029] Furthermore, the system has image generating means for
generating a first 2D photographic image from the first 3D point
cloud, and a second 2D photographic image from the second 3D point
cloud, respectively, and registration means for non-rigidly
aligning the first 2D photographic image and the second 2D
photographic image so as to track the brain deformation. In one
embodiment, the image generating means comprises a computer. The
registration means comprises a controller.
[0030] These and other aspects of the present invention will become
apparent from the following description of the preferred embodiment
taken in conjunction with the following drawings, although
variations and modifications therein may be affected without
departing from the spirit and scope of the novel concepts of the
disclosure.
[0031] These and other aspects of the present invention will become
apparent from the following description of the preferred embodiment
taken in conjunction with the following drawings, although
variations and modifications therein may be affected without
departing from the spirit and scope of the novel concepts of the
disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0032] FIG. 1 is a flowchart for registering an image volume of a
brain of a living subject to a cortical surface of at least one
targeted region of the brain of the living subject according to one
embodiment of the present invention.
[0033] FIG. 2 shows a texture mapping process for generating a
grayscale encoded cortical brain surface from a pre-operative MR
image volume: (a) a ray-casting and voxel averaging algorithm
applied to a segmented pre-operative MR brain volume, and (b) a
resultant grayscale encoded cortical brain surface (i.e., textured
point cloud) from the segmented pre-operative MR brain volume.
[0034] FIG. 3 shows a cortical surface registration according to
one embodiment of the present invention: (a) a grayscale encoded
cortical brain surface generated from a pre-operative MR image
volume of a living subject, (b) a textured point cloud
intra-operatively acquired by a laser range scanner from a targeted
region of interest of the living subject, and (c) an alignment of
the textured point cloud to the grayscale encoded cortical brain
surface.
[0035] FIG. 4 shows a schematic framework for tracking brain shift
using a laser range scanner according one embodiment of the present
invention.
[0036] FIG. 5 shows a watermelon phantom used for elaborating
accuracy of a cortical surface registration according to one
embodiment of the present invention: (a) a watermelon with contrast
regent soaked twine laid into carved vessel grooves, (b) the
watermelon with a fiducial marker placed at a predetermined
position, and (c) the watermelon with an Acustar divot cap placed
at a predetermined position.
[0037] FIG. 6 shows volume rendering of image data having fudicial
makers and localized target points: (a) fudicial markers A-F and
manually localized landmarks 1-15 in the image space and Optotrak
coordinate systems, and (b) landmarks localized in LRS coordinate
system.
[0038] FIG. 7 shows a simulated deep tissue sampling according to
one embodiment of the present invention: (a) a front view of the
deep tissue sampling region, and (b) a side view of the deep tissue
sampling region.
[0039] FIG. 8 shows a TRE histogram for deep tissue targets using
PBR on surface landmarks, ICP registration on surface contours, and
SurfaceMI on textured surfaces, respectively, according to one
embodiment of the present invention.
[0040] FIG. 9 shows a 3D distribution of the TRE for deep tissue
targets as shown in FIG. 8: (a), (b), and (c) a top view of the
watermelon surface with the TRE distribution using PBR, ICP, and
SurfaceMI, respectively, (d), (e), and (f) a front view of the
watermelon surface with the TRE distribution using PBR, ICP, and
SurfaceMI, respectively.
[0041] FIG. 10 shows intermodality registration results of two
textured surfaces using ICP and SurfaceMI, respectively, according
to one embodiment of the present invention: (a) ICP registration
with a given initial landmark perturbation, (b) ICP registered, (c)
SurfaceMI registration with a given initial landmark perturbation,
and (d) SurfaceMI registered.
[0042] FIG. 11 shows a FRE histogram with a given initial landmark
perturbation according to one embodiment of the present invention.
The landmarks in the FOV were perturbed up to .+-.2.5.degree. in
each spherical coordinate (.phi., .phi., .theta.) in an image
space.
[0043] FIG. 12 shows an intra-operative LRS data acquired from a
first patient according to one embodiment of the present invention:
(a) digital photographic image with the vein of Trolard
highlighted, and (b) a textured point cloud generated
intra-operatively by a LRS.
[0044] FIG. 13 shows registration results from intra-operative data
according to one embodiment of the present invention: (a) PBR using
manually localized landmarks in an image space and a LRS space, (b)
ICP registration using highlighted contours in the image space and
the LRS space, and (c) SurfaceMI registration given the initial
alignment provided by the PBR method. The highlighted contours are
prominent sulcal and vessel patterns visible in both spaces.
[0045] FIG. 14 shows an intra-operative LRS data and pre-operative
data acquired from a second patient, respectively: (a) digital
photographic image of the scanning FOV, (b) texture image captured
at the time of range scanning, (c) texture point cloud of the
intra-operative surgical FOV generated via range scanning and
texture mapping, and (d) textured point cloud generated from the
pre-operative image via ray casting.
[0046] FIG. 15 shows registration results using the data of FIG.
14: (a) using cortical surface landmarkers and PBG, (b) using ICP
transforms on the two surfaces, and (c) SurfaceMI registration. The
LRS point cloud has been artificially texture to enhance
contrast.
[0047] FIG. 16 shows an intra-operative LRS data and pre-operative
data acquired from a third patient, respectively: (a) digital
photographic image of the scanning FOV, (b) texture image captured
at the time of range scanning, (c) texture point cloud of the
intra-operative surgical FOV generated via range scanning and
texture mapping, and (d) textured point cloud generated from the
pre-operative image via ray casting.
[0048] FIG. 17 shows registration results using the data of FIG.
16: (a) using cortical surface landmarkers and PBG, (b) using ICP
transforms on the two surfaces, and (c) SurfaceMl registration. The
LRS point cloud has been artificially texture to enhance
contrast.
[0049] FIG. 18 shows undeformed and deformed images after rigid and
non rigid registration for a first in vivo case: (a) a textured
point cloud of a targeted region of interest acquired at time
t.sub.1, (b) a textured point cloud of the targeted region of
interest acquired at time t.sub.2 later than t.sub.1, (c) a result
of a rigid body registration of the textured point cloud (a) to the
textured point cloud (b), and (d) a result of both a rigid body
registration and nonrigid registration of the textured point cloud
(a) to the textured point cloud (b).
[0050] FIG. 19 shows undeformed and deformed images after rigid and
non rigid registration for a second in vivo case: (a) a textured
point cloud of a targeted region of interest acquired at time
t.sub.1, (b) a textured point cloud of the targeted region of
interest acquired at time t.sub.2 later than t.sub.1, (c) a result
of a rigid body registration of the textured point cloud (a) to the
textured point cloud (b), and (d) a result of both a rigid body
registration and nonrigid registration of the textured point cloud
(a) to the textured point cloud (b).
[0051] FIG. 20 shows undeformed and deformed images after rigid and
non rigid registration for a third in vivo case: (a) a textured
point cloud of a targeted region of interest acquired at time
t.sub.1, (b) a textured point cloud of the targeted region of
interest acquired at time t.sub.2 later than t.sub.1, (c) a result
of a rigid body registration of the textured point cloud (a) to the
textured point cloud (b), and (d) a result of both a rigid body
registration and nonrigid registration of the textured point cloud
(a) to the textured point cloud (b).
DETAILED DESCRIPTION OF THE INVENTION
[0052] The present invention is more particularly described in the
following examples that are intended as illustrative only since
numerous modifications and variations therein will be apparent to
those skilled in the art. Various embodiments of the invention are
now described in detail. Referring to the drawings, like numbers
indicate like parts throughout the views. As used in the
description herein and throughout the claims that follow, the
meaning of "a," "an," and "the" includes plural reference unless
the context clearly dictates otherwise. Also, as used in the
description herein and throughout the claims that follow, the
meaning of "in" includes "in" and "on" unless the context clearly
dictates otherwise. Moreover, titles or subtitles may be used in
the specification for the convenience of a reader, which has no
influence on the scope of the invention. Additionally, some terms
used in this specification are more specifically defined below.
Definitions
[0053] The terms used in this specification generally have their
ordinary meanings in the art, within the context of the invention,
and in the specific context where each term is used.
[0054] Certain terms that are used to describe the invention are
discussed below, or elsewhere in the specification, to provide
additional guidance to the practitioner in describing various
embodiments of the invention and how to practice the invention. For
convenience, certain terms may be highlighted, for example using
italics and/or quotation marks. The use of highlighting has no
influence on the scope and meaning of a term; the scope and meaning
of a term is the same, in the same context, whether or not it is
highlighted. It will be appreciated that the same thing can be said
in more than one way. Consequently, alternative language and
synonyms may be used for any one or more of the terms discussed
herein, nor is any special significance to be placed upon whether
or not a term is elaborated or discussed herein. Synonyms for
certain terms are provided. A recital of one or more synonyms does
not exclude the use of other synonyms. The use of examples anywhere
in this specification, including examples of any terms discussed
herein, is illustrative only, and in no way limits the scope and
meaning of the invention or of any exemplified term. Likewise, the
invention is not limited to various embodiments given in this
specification.
[0055] As used herein, "around", "about" or "approximately" shall
generally mean within 20 percent, preferably within 10 percent, and
more preferably within 5 percent of a given value or range.
Numerical quantities given herein are approximate, meaning that the
term "around", "about" or "approximately" can be inferred if not
expressly stated.
[0056] As used herein, the term "living subject" refers to a human
being such as a patient, or an animal such as a lab testing
monkey.
[0057] As used herein, the term "field of view" (hereinafter "FOV")
refers to an extent of a visible image field of a region of
interest of a living subject under treatment or test.
[0058] As used herein, "registration," and "alignment" are synonyms
in the specification.
OVERVIEW OF THE INVENTION
[0059] The present invention, in one aspect, relates to a method of
registering an image volume of a brain of a living subject to a
cortical surface of at least one targeted region of the brain of
the living subject. Referring to FIGS. 1-3 and first to FIG. 1, the
method, in one embodiment, includes the following steps: at step
101, the image volume is acquired pre-operatively from the brain of
the living subject, where the image volume of the brain of the
living subject comprises image data with respect to the brain
surface geometry. The image data with respect to the brain surface
geometry, in one embodiment, is obtained through the use of at
least one of positron emission tomography (hereinafter "PET"),
electroencephalography, computer tomography, functional magnetic
resonance (hereinafter "fMR") imaging and magnetic resonance
imaging.
[0060] At step 103, a grayscale encoded brain surface is generated
from the acquired image volume. In one embodiment, the generating
step comprises the steps of segmenting the acquired image volume of
the brain of the living subject, extracting a point cloud
representation of the brain surface geometry from the segmented
image volume, and performing a ray-casting and voxel intensity
averaging on the point cloud representation so as to generate a
grayscale encoded brain surface that contains intensity patterns
representing sulcal-gyrus differences and contrast-enhanced
vasculature.
[0061] Referring now to FIG. 2, a location of a resection surface
on a MR image 210 pre-operatively acquired from a brain of a
patient is identified, for example, surface 220, according to a
pre-operative surgical plan. The MR image volume 210 is segmented
and the surface 220 of the segmented MR image volume is shown in
FIG. 2a. From the segmented MR image volume, a point cloud
representation of the brain surface geometry is extracted.
Specifically, the surface 220 of the segmented MR image volume is
positioned orthogonal to a ray-casting source having a ray 230. A
ray-casting algorithm combined with voxel intensity averaging 260
is employed to grayscale encode the point cloud. In one embodiment,
the voxel intensity averaging process averages 3 to 5 voxel
intensities along the ray 230. At the conclusion of this process,
the patient's cortical image surface is rendered into a textured
point cloud 250 that contains intensity patterns representing
sulcal-gyrus differences as well as contrast-enhanced vasculature,
as shown in FIG. 2b. For the point clouds generated via the ray
casting algorithm, the mean and median point-to-point distances are
0.7 and 0.6 mm, respectively.
[0062] Referring back to FIG. 1, at step 105, a textured point
cloud of the cortical surface is obtained intra-operatively from
the at least one targeted region of the brain of the living
subject. The step of obtaining a textured point cloud of the
cortical surface is performed with an optical device that is
capable of obtaining frequency, intensity and geometric data with
respect to the cortical surface simultaneously. In one embodiment,
the optical device is a LRS, for example, RealScan3D, (3D Digital
Corporation, Bedford Hills, N.Y.). The ability to rapidly capture
both geometric and color-intensity information from an
intra-operative brain surface has made a laser range scanner, in
conjunction with cortical surface registrations, to be a very
promising tool for tracking of brain deformation. For example,
Nakajima et al. [33] has demonstrated an average of 2.3.+-.1.3 mm
fiducial registration error using cortical vessels scanned with a
LRS for registration. Also, some preliminary work using a scanning
based system for cortical surface geometric registration has been
reported but a systematic evaluation has not been performed to date
[5]. Great clinical relevance would be gained if geometric and
intensity information from an intra-operative brain surface could
be invasively captured and effectively aligned to a pre-operative
patient-specific image so as to track brain deformation for
guidance during surgery. The LRS is capable of optically scanning
an exposed brain surface of the living subject during surgery with
a laser, capturing surface-reflected light from the brain surface
of the living subject, acquiring a point cloud representation of
the geometry of the cortical surface from the captured
surface-reflected light, and color-encoding the acquired point
cloud representation with intensity values of a field of view so as
to obtain a textured point cloud of the cortical surface of the at
least one targeted region of the brain.
[0063] With respect to an intra-operative acquisition of data, a
calibration object is routinely scanned prior to registration so as
to ensure operational fidelity of the LRS. At select times during
the surgery, after durotomy, the LRS is positioned over the exposed
brain surface and operated by passing a laser stripe continuously
over the exposed brain surface in approximately 5-7 seconds. The
laser output is detected by a first digital camera such as a high
resolution charge-coupled device (hereinafter "CCD") camera of the
LRS and principles of triangulation are used to determine the 3D
location of each illuminated point so as to construct the point
cloud. Following the laser-stripe pass, a second color CCD camera
of the LRS is used to acquire a red-green-blue (hereinafter "RGB")
bitmap image of the surgical FOV, which is used to color-encode
each 3D point so as to obtain the textured point cloud of the
surgical FOV. The mean and median point-to-point distances for the
range-scan point clouds are 0.65 and 0.6 mm, respectively.
[0064] At step 107, the grayscale-encoded brain surface is aligned
to the textured point cloud of the cortical surface so as to
register the image volume of the brain with respect to the cortical
surface of the at least one targeted region of the brain. FIG. 3
shows the alignment 330 of the grayscale-encoded brain surface 310
to the textured point cloud 320 of the cortical surface. In one
embodiment, the alignment of the grayscale-encoded brain surface to
the textured point cloud of the cortical surface is carried out by
registering the grayscale-encoded brain surface of the brain to the
textured point cloud of the cortical surface of the targeted region
of the brain using an iterative closest point (hereinafter "ICP")
algorithm.
[0065] The registration, in one embodiment, includes the following
steps: (a) corresponding points from the grayscale-encoded brain
surface of the brain and the textured point cloud of the cortical
surface of the targeted region of the brain are paired according to
a closest distance metric, (b) a point-based registration is
executed, (c) the closest distance metric is updated accordingly.
And then steps (a)-(c) are repeated until a disparity function d
satisfies a specified tolerance, wherein the disparity function d
has the form of: 3 d = 1 N j N ; y j - T ( x j ) r; 2 , ( 1 )
[0066] where T(x.sub.j) represents a rigid transformation of a
point, x.sub.j, on a source surface such as the grayscale-encoded
brain surface to a corresponding point, y.sub.j, on a target
surface such as the textured point cloud of the cortical surface,
and N is the number of points in the source surface. The mean
residual distance between counter points in each of the
grayscale-encoded brain surface and the textured point cloud of the
cortical surface is used as the closest distance metric of
registration accuracy. To calculate this metric, correspondence
between target cloud counter points and transformed source cloud
counter points is established via nearest neighbor calculation.
Mean registration error (hereinafter "MRE") is defined by a
disparity function (1).
[0067] Although excellent at aligning geometrically unique
surfaces, ICP registration generally has difficulty with an
intra-operative environment if relied upon solely. In reality, not
all regions of the brain surface can be expressed as a unique
geometry with respect to visible sulcal/fissure features of the
intra-operatively exposed brain. Pathology, such as a tumor, can
also influence the initial shape of the brain surface dramatically.
In addition, the fidelity of image segmentation can also become a
potential source of misalignment. Thus, a necessary step of the
alignment of the two point clouds is to optimize the ICP
registration.
[0068] The optimization of the ICP registration according to one
embodiment is to normalize or optimize the mutual information
(hereinafter "MI") of the two point clouds, which includes the step
of choosing a normalized mutual information (hereinafter "NMI") in
the form [34] of 4 NMI ( x , y ) = H ( x ) + H ( y ) H ( x , y ) ,
( 2 )
[0069] where H(x) and H(x, y) are the marginal and joint entropies
of the point clouds, respectively. In addition, the closest
distance metric is used to determine proper intensity
correspondence between a source surface and a target surface. To
aid the optimization process, the source cloud is constrained to
move along the surface of a sphere fitted to the target cloud. The
constraint reduces the degrees of freedom from six in Cartesian
coordinates (position and orientation) to three in sphere
coordinates (azimuth, elevation and roll). Finally, the normalized
mutual information is optimized by using an iterative procedure. In
one embodiment, the iterative procedure includes the Powell's
iterative method [35].
[0070] The registration algorithm of the present invention is
referred to a SurfaceMl registration in the specification.
[0071] In another aspect, the present invention relates to a system
for cortical surface registration related to a diagnostic or
surgical procedure. The system, in one embodiment, has an imaging
acquiring device for pre-operatively acquiring an image volume of a
targeted region of a brain of a living subject from which a first
textured point cloud of the cortical surface, for example, a
grayscale encoded brain surface, is derived. A conventional imaging
scanner for obtaining one of PET, electroencephalography, CT, fMR
and MR images can be used to practice the invention.
[0072] One critical component in the system for cortical surface
registration related to a diagnostic or surgical procedure is a
rapid acquisition of geometric data that describes the deforming
nature of the brain during surgery. A LRS, for example, RealScan3D,
is capable of capturing 3D topography of a target of interest as
well surface texture mapping to submillimeter accuracy.
[0073] The RealScan3D is lightweight, compact, and has a standard
tripod mount with a volume 9.5".times.12.5".times.3.25" and weight
4.5 lbs. For a clinical use, the RealScan3D is equipped with a
customized vibration-damping monopod, and/or attached to a surgical
arm within the OR. The scanning field of the RealScan3D has 512
horizontal points by 500 vertical points per scan and is
accomplished in approximately 5 s to 7 s. The laser used in the LRS
is a Class-I "eye-safe" 6.7 mW visible laser. The laser stripe
generator has an adjustable fan-out angle (maximum fan-out is
30.degree.) and acquires each stripe at approximately 60 Hz. The
LRS accuracy is 300 .mu.m at a position that is 30 cm far from a
targeted region of interest and approximately 1000 .mu.m at a
position that is 80 cm far from the targeted region of
interest.
[0074] In one embodiment, the LRS is brought to between 30 cm to 45
cm of the target. The complete process of moving the LRS into the
field of view (hereinafter "FOV"), acquiring a scan, and exiting
from the FOV takes approximately 1 to 1.5 min, which includes laser
light and fan-out angle adjustments. In general, an impact of the
LRS on the OR is negligible. The LRS is actively tracked in the OR
space using a spatial tracking system, such as Optotrak.RTM. 3020,
(Northern Digital, Inc., Waterloo, Canada), and calibrated using
phantoms with separate independent digitization. Additionally,
prior to clinical data acquisition, the use of the LRS on human
patients is approved by the Vanderbilt University Institutional
Review Board (hereinafter "VUIRB") and patient consent is acquired
for all clinical data.
[0075] The system also has a computer for receiving and processing
data related to the first textured point cloud of the cortical
surface and the second textured point cloud of the cortical surface
so as to register images of the brain of the living subject to the
cortical surface of the living subject. The system further includes
a display device coupled to the computer for displaying the
cortical surface registration dynamically to facilitate the
diagnostic or surgical procedure. Any type of computers, such as
personal computer, laptop, and supercomputer, and displays, such as
display monitor, and liquid crystal display, can be employed to
practice the current invention.
[0076] In a further aspect, the present invention relates to a
method of deformable cortical surface registration related to a
diagnostic or surgical procedure to track brain deformation.
[0077] Referring to FIG. 4, the method according to one embodiment
of the present invention includes the following steps: at step 410,
a first 3D point cloud 415 of a brain of a living subject is
obtained prior to or during brain deformation, where each 3D point
of the first 3D point cloud 415 is color-encoded. The step of
obtaining a first 3D point cloud 415 includes the steps of
optically scanning an exposed brain surface 405 of the living
subject at a time prior to or during brain deformation, capturing
surface-reflected light from the brain surface of the living
subject, acquiring a first point cloud representation of the
geometry of the cortical surface from the captured
surface-reflected light, and color-encoding the acquired each point
of the first point cloud representation by a direct linear
transform representation so as to construct the first 3D point
cloud 415. In one embodiment, the step of obtaining a first 3D
point cloud 415 is performed with an optical device, for example, a
LRS 401, which is capable of obtaining frequency, intensity and
geometric data with respect to the cortical surface simultaneously.
The data provided by the LRS includes the first 3D point cloud,
where each 3D point is color-encoded from a RGB photographic image
of the FOV acquired at the time of scanning by a direct linear
transform (hereinafter "DLT"). The DLT of the LRS is determined at
the factory.
[0078] At step 420, a DLT mapping between the first 3D point cloud
(a physical space) and the first 2D photographic image (a image
space) is calculated from the abundance of data acquired by the LRS
so as to generate a first 2D photographic image 417 from the first
3D point cloud 415. Since the LRS is tracked using an Optotrak 3020
and the DLT is known, any photographic image plane can be
reconstructed from the LRS digital image data.
[0079] Repeating steps 410 and 420 during or after brain
deformation will obtain a second 3D point cloud 435 of the brain
(step 430), wherein each 3D point of the second 3D point cloud
representation is color-encoded, and generate a second 2D
photographic image 437 from the second 3D point cloud 435 (step
440), respectively. In one embodiment, steps 410 and 430 are
sequentially performed with a time difference, .DELTA.t. That is,
the first 2D photographic image 417 acquired in SCAN 1 (step 410)
represents an image of the FOV before brain deformation has taken
place, and the second 2D photographic image 437 acquired in SCAN 2
(step 430) represents an image of the FOV after brain shift has
taken place.
[0080] At step 450, the first 2D photographic image 417 and the
second 2D photographic image 437 are non-rigidly aligned to
generate a non-rigidly registered SCAN 1 photographic image 467.
Finally, at step 460, a depth map, .delta..sub.1, acquired in SCAN
2 (step 430) is applied to the non-rigidly registered SCAN 1
photographic image 467, so as to provide a measurement of shift
from the pre-shift scanning (SCAN 1) to the post-shift scanning
(SCAN 2).
[0081] Specifically, the step of non-rigidly aligning the first 2D
photographic image 417 and the second 2D photographic image 417
includes the steps of transforming the first 2D photographic image
417 and the second 2D photographic image 437 from RGB images into
corresponding gray level images, and obtaining a final deformation
field that registers gray level images one to the other. In one
embodiment, the step of obtaining a deformation field comprises the
steps of calculating a deformation field for each of a series of
levels, wherein each level is corresponding to a particular
combination of scale and resolution for an image, and adding all
the deformation fields for all of the series of levels to generate
the final deformation field.
[0082] In practice, the creation of the similar photographic image
plane is not always necessary. This translates to the nonrigid
registration algorithm accounting for deformation as well as
scanner movement, i.e. the acquisition of a different FOV within
the photographic image due to a slight difference in the laser
scanner's spatial position is accounted for in the nonrigid
registration process.
[0083] These and other aspects of the present invention are further
described below.
METHODS, IMPLEMENTATIONS AND EXAMPLES OF THE INVENTION
[0084] For the purposes of comparison and feasibility, conventional
methods of cortical surface registration were also performed. For
example, for the approach of Nakajima et al. [33], cortical
features such as vessel bifurcations are localized in both MR and
scanner image spaces and a rigid PBR is then performed between the
two spaces. Another registration framework is based on the ICP,
where the registration targets became vessel and sulcal contours
visible on the MR image and the laser-scanned cortical surface.
This suite of registration approaches provides multiple avenues to
pursue for determining an optimal cortical surface alignment under
varying surgical conditions.
[0085] Without intent to limit the scope of the invention, further
exemplary procedures and experimental results of same according to
the embodiments of the present invention are given below.
Example 1
Phantom Experiment
[0086] To evaluate the accuracy and effectiveness of the SurfaceMI
algorithm to register intermodality surfaces, a phantom experiment
using a watermelon was conducted. Referring to FIGS. 5a-5c, in the
experiment, Omnipaque (Amersham Health PLC, Buckinghamshire, the
United Kingdom) soaked twine 510 was laid into the watermelon
surface 520 to simulate the appearance of contrast-enhanced
vasculature on the brain surface in CT, and/or MR imaging. Rigid
fiducial markers 530, such as Acustar.RTM. (Z-Kat, Inc., Hollywood,
Fla.), were implanted into the watermelon surface 520 for alignment
of one image space to another one. The Acustar.RTM. fiducial
markers 530 were filled with CT and/or MR visible contrast
enhancement liquid. In addition, Acustar.RTM. divot caps 540 were
placed at soaked twine (vessel) bifurcations for target
localization. The phantom was imaged by a CT imager, such as
Mx8000, (Philips Medical Systems, Bothell, Wash.), and scanned by a
LRS, e.g., RealScan3D, and digitized by a spatial tracking system
such as Optotrak.RTM. 3020, respectively. Therefore, the phantom
was represented by three coordinate systems including a CT image
coordinate system, an Optotrak coordinate system, and a LRS
coordinate system. Other fiducial markers, CT imagers, LRS and
spatial tracking systems can also be used to practice the current
invention.
[0087] It is crucial to accurately to localize targets of interest
during surgery for the IGS. For comparison, various registrations
were performed, and fiducial registration errors (hereinafter
"FRE") and target registration errors (hereinafter "TRE"), as
defined by Mandava and Fitzpatrick [36, 37], were examined. The
first registration aligned the CT image space coordinate system,
img, to the Optotrak coordinate system, opto, using the
Acustar.RTM. fiducial markers in each modality. The alignment is to
find a transformation from the image space coordinate system to the
Optotrak coordinate system, T.sub.img.fwdarw.opto. FRE and TRE were
calculated for the registration so as to provide an optimal
registration of a physical space to an image space. FIG. 6a shows
the locations of the six fiducial markers A-F and fifteen manually
identified target points 1-15 in a volume rendering of an image of
the watermelon phantom.
[0088] Having established this registration optimum, corresponding
sets of manually identified points at vessel bifurcations in img
and opto were registered to provide quantitative validation of
Nakajima's method of using cortical features for registering the
physical space to the image space. Additionally, ten visible
bifurcation points 1b, 3b, 4b, 5b, 7b-9b, and 12b-14b in a LRS
space, lrs, were localized, as shown in FIG. 6b. These points
respectively correspond to manually identified points 1, 3, 4, 5,
7-9, and 12-14 in img and opto, as shown in FIG. 6a, and used for
the PBR registration as a verification of Nakajima's method applied
to the LRS data. FRE was calculated for all registrations, i.e.,
T.sub.img.fwdarw.opto, T.sub.img.fwdarw.lrs, and
T.sub.opto.fwdarw.lrs. The manually identified target points in
each space were localized three times and averaged to minimize
localization error.
[0089] The other methods for intra-operative registration were also
examined within the context of the phantom experiment. The ICP
registration was performed using phantom vessel contours extracted
using simple threshold from the LRS and CT data. The SurfaceMI
framework was used to align the segmented surface. For each
registration, a reduced region of the watermelon LRS surface was
extracted to simulate the approximate size of the surgical FOV. For
both registration methods of the ICP and SurfaceMI, an initial
alignment of the surfaces was provided by using three manually
localized targets visible in the segmented surface. TRE was
calculated in both registration frameworks using seven novel
surface targets (i.e., those landmarks that were not in the
surgical FOV) and was compared to the TRE provided by the PBR
alignment of vessel landmarks.
[0090] Robustness studies for the registration frameworks were
carried out by perturbing initial landmarks uniformly along the
surface of a sphere fitted to the target point cloud, i.e.,
perturbing the landmarks in spherical coordinates .phi., .theta.
and .phi. at the fitted radius r. The perturbations were
independently and uniformly sampled from -2.5.degree. to
2.5.degree. (simulates approximately 1 cm fiducial localization
error, i.e., perturbation arc length r.sub..THETA.=9.29 mm) in each
spherical axis for each trial, and each framework was subject to
500 perturbation trials. The results of this experiment provide
insight as to the efficacy of the registration frameworks given
suboptimal initial conditions.
[0091] Accuracy of the registration frameworks with regard to deep
tissue targets was also investigated. For this experiment, deep
tissue targets were sampled in a 5 cm radius of the centroid of the
manually localized surface points. The sampling was constrained to
only deep tissue targets, i.e., sample points which lie in both the
sphere and watermelon, as shown in FIG. 7. The larger sphere 710
demonstrates the geometric sphere fit of the point cloud 730. The
smaller sphere 720 represents a sampling region with radius of 5
cm, centered about the centroid of the localized fiducials. The
volume of overlap demonstrates the deep tissue sampling region.
True positions of the deep tissue targets were found in LRS space
by transforming targets from image space using the rigid-body
transformation T.sub.img.fwdarw.lrs (based on identifying vessel
points in both modalities). These same tissue targets in image
space were also registered to LRS using transformations based on
SurfaceMI which when compared served as an estimate of TRE.
[0092] The registration results achieved with implantable markers
were comparable to previously published data [1]. Using the
Acustar.RTM. fiducial marker system, the mean FRE of 0.3.+-.0.1 mm
was achieved using six markers. The mean TRE for this registration
was 1.7.+-.0.3 mm using fifteen target landmarks. These results
demonstrate the accuracy associated with implantable fiducial
markers and provide a baseline for comparison with subsequent
registrations.
1TABLE 1 TRE for the three registration methods, PBR, ICP, and
SurfaceMI, in the watermelon phantom experiment on a LRS surface
that approximates a surgical FOV. Three landmarks were used as
fiducials and seven targets were used to calculate TRE.
Registration Method Mean TRE (mm) PBR 2.6 .+-. 0.7 ICP 2.4 .+-. 0.8
SurfaceMI 2.5 .+-. 0.7
[0093] The registration results for the phantom experiment
concerned with the alignment of the cortical surface using
vessel-based landmarks show excellent correlation with the
previously published studies of Nakajima et al. [33]. FRE for ten
manually localized landmarks in all three spaces, i.e., opto, img,
and lrs, was 1.3.+-.0.5 mm and 1.7.+-.0.6 mm for
T.sub.img.fwdarw.opto and T.sub.img.fwdarw.lrs, respectively. In
addition, a second PBR was calculated using a subset of the vessel
markers in a focal cortical region to simulate vessel fiducials
within a craniotomy. The remaining vessel bifurcations outside the
simulated surgical FOV were used as targets. The TRE is listed in
Table 1.
[0094] As an aside, a measurement of localization precision was
calculated since each set of landmarks (i.e., in img, opto, and
Irs) was identified three times. Precision was measured as the mean
standard deviation for each measurement (x, y, z) in corresponding
landmarks across the three trials. For the landmarks selected in
img, the mean standard deviations in x, y, and z were 0.27, 0.28,
and 0.31 mm, respectively. In opto, the mean standard deviation in
x, y, and z are 0.35, 0.22, and 0.13 mm, respectively. For the ten
landmarks chosen in lrs, the mean standard deviations in x, y, and
z were 0.71, 0.58, and 1.14 mm.
[0095] Referring now to FIGS. 8-11 and first to FIG. 8, a histogram
and mean TRE for simulated deep tissue targets is shown. Bars 810,
820 and 830 shown in FIG. 8 represent the TRE histogram for deep
tissue targets using the PBR-based registration on surface
landmarks, the ICP-based registration on surface contours, and the
SurfaceMI on textured surfaces, respectively. Corresponding mean
TRE 850 of the deep tissue targets using the PBR-based registration
on surface landmarks, the ICP-based registration on surface
contours, and the SurfaceMI on textured surfaces are 1.2.+-.0.3 mm,
2.0.+-.0.3 mm, 1.0.+-.0.2 mm, respectively. FIG. 9 shows a 3D
distribution 930 of the TRE for the deep tissue targets shown in
FIG. 8 overlaying the watermelon image volume 920. FIGS. 9a, 9b and
9c are a top view of the 3D TRE distribution 920 for the deep
tissue targets using the PBR-based registration on surface
landmarks, the ICP-based registration on surface contours, and the
SurfaceMI on textured surfaces, respectively, while FIGS. 9d, 9e
and 9f are a side view of the 3D TRE distribution 920 corresponding
to FIGS. 9a, 9b and 9c, respectively. Each deep tissue sample of
the TRE distribution 920 is grayscale encoded on the watermelon s
image volume 930 with the range of scalar values of the TRE being
shown in a bar 910 associated with each figure. The results shown
in FIGS. 8 and 9 suggest that SurfaceMI predicts the deep tissue
targets more accurately then the PBR and ICP registration
methods.
[0096] In addition to reporting registration results based on a
routine application of each alignment framework, a series of
robustness studies was performed to investigate the effects of
varied initial guesses (i.e., an approximate 1 to 6 mm fiducial
localization error with individual fiducial error as large as 9.3
mm). Examples of the registration provided by the ICP and the
SurfaceMI with a given initial landmark perturbation are shown in
FIG. 10. The ICP registration with a perturbed initial condition
1010 and the ICP registered condition 1020 is shown in FIGS. 10a
and 10b, respectively, while a SurfaceMI registration with a
perturbed initial condition 1030 and a SurfaceMI registered
condition 1040 are shown in FIGS. 10c and 10d, respectively. It
should be noted that there is a texture projected on the surface of
the watermelon that is an artifact of the rendering process, i.e.,
this texture did not affect the registration process. A gross-scale
representation of the texture, which is a result of the
slice-to-slice spacing in the CT image, can be seen in FIG. 6a for
comparison. FRE results from these perturbation studies using the
PBR, the ICP, and the SurfaceMI registrations on the same cortical
sub-region used for the TRE studies of Table 1 are shown in FIG. 11
over 500 trials. The landmarks in the FOV were perturbed up to
.+-.2.5.degree. in each spherical coordinate (.phi., .phi.,
.theta.) in the image space. In FIG. 11, bars 1110, 1120 and 1130
represent a histogram of the FRE using the PBR, the ICP, and the
SurfaceMI registrations, respectively. Corresponding mean FRE 1150
using the PBR, the ICP, and the SurfaceMI registrations are
3.0.+-.0.8 mm, 1.7.+-.0.3 mm, 3.4.+-.11.9 mm, respectively. As
shown in FIG. 11, the distribution of the FRE ranges from 1.0 to
5.8 mm for the three landmarks used in an initialization of the ICP
and SurfaceMI registrations. For the ICP registration on the
surface contours, the FRE is reduced by approximately 43%. While
the SurfaceMI registration produces some outliers. Using the
extreme studentized deviate (hereinafter "ESD") [38], eight
outliers were detected with >99.95% confidence. Removing these
outliers from the SurfaceMI trials produced a mean FRE of
2.2.+-.0.8 mm, reducing FRE by approximately 27%.
[0097] In a summary, initial studies using rigid markers were
performed to provide baseline registration accuracy with respect to
unknown errors associated with the phantom and/or imaging method;
results reflected comparable accuracies reported in the literature
[1]. The next set of studies used vessel bifurcations localized in
all modalities as the basis for registration. Reassuringly, the FRE
between img and opto using the manually localized vessel
bifurcations were comparable to values reported by Nakajima et al.
Similar values were also determined when registering vessel
bifurcations using LRS data within the context of PBR, ICP, and
SurfaceMI. This would indicate that using techniques similar to
Nakajima et al. should be achievable using LRS data. In addition to
reporting error within the simulated craniotomy region, targets
outside the focal region were also used to assess alignment
quality. Overall, the difference between results among all three
methods was negligible. The increased magnitude of TRE over FRE
agrees with an accepted understanding regarding the effects of
fiducial placement on target registration error; that is, even with
a low FRE, a sparse number of fiducials localized within a
concentrated area can precipitate a "lever-arm" effect in areas
remote to the registration region. Interestingly, a different
result is seen with respect to targets in close proximity to the
subregion of interest on the watermelon surface. FIG. 8 reports the
distribution of TRE data compared among all three registration
approaches. With respect to the mean TRE error for the entire
region, SurfaceMI performed the best with an average TRE of 1.0 mm.
When comparing deep tissue results between the PBR and SurfaceMI
methods, as shown in FIG. 9, PBR has a greater range of TRE error
than SurfaceMI, which may be due to the difficulty in localizing
bifurcations upon the LRS data for PBR methods. The ICP
registration performed considerably worse, and this may be due to
the contour threshold process. More specifically, any spatial noise
contained within the thresholded vessel structure is not averaged
out as well within the ICP framework when compared to using a
denser point cloud. This possible source of error would not be
present within the SurfaceMI approach since the dense geometric
data are maintained and the fine adjustments to alignment are
provided by an intensity-based registration. SurfaceMI and PBR
produced comparable results although the TRE spatial distribution
for deep tissue targets was greater for the PBR method. This may
suggest that the effects of a combined surface and intensity
approach produce a lower error due to the averaging effects
associated with the registration metrics used in SurfaceMl. When
comparing SurfaceMI to ICP, the results suggest that vessel
contours alone may not be the best approach to cortical surface
registration, but rather, the addition of the intensity data
provides significant refinement to the alignment.
Example 2
Clinical Trials
[0098] For clinical data acquisitions, the LRS approved by the
VUIRB uses a Class I "eye-safe" laser and is mounted on a vibration
damping monopod. Immediately after duratomy, the LRS is brought
into the surgical FOV and the scanning extents (left and right
scanning margins) are set to cover the width of the craniotomy. A
laser stripe is then passed over the FOV and sampled 500 times
between extents. Each sampling of the line produces 256 3D sample
points, which results a practical point cloud density of
approximately 100,000 points per scan. Immediately following the
range scan, an RGB-bitmap (texture) of the scanning FOV is acquired
and texture map coordinates are assigned to each 3D point via
manufacturer calibration. Data acquisition by the LRS takes on the
order of 15 to 30 seconds, with approximately 1.5 minutes of
intra-operative time spent per scan (i.e. bring the LRS into the
surgical FOV, calibrate extents and acquire, then remove from the
FOV). Clinical data acquisitions for three patients are highlighted
as follows.
[0099] The first patient was a 37-year old man with a six-week
history of focal motor seizures. MR imaging revealed a hypointense
nonenhancing mass in the posterior, superior left frontal lobe,
abutting the motor strip. An awake resection was operated for the
patient, with motor and speech mapping. Intra-operatively, he was
placed in the supine position, with the vertex of the head elevated
15.degree. and the head turned 30.degree. to the right. A
fronto-temporal-parietal craniotomy was performed and the tumor was
localized using ultrasound and frameless stereotaxy. The vein of
Trolard coursed superiorly to the superior sagittal sinus,
immediately behind the posterior extent of the tumor and directly
in front of the motor gyrus. After mapping of the speech and motor
regions of the face and arm, gross total resection of the tumor was
accomplished. The patient tolerated the procedure without
neurological sequelae. Intra-operatively, following durotomy, the
LRS such as RealScan3D was moved into position via the customized
monopod above the craniotomy site at approximately 30 to 45 cm from
the brain's surface. The LRS was activated and acquired
approximately 20,000 points in 5 to 7 s. Following retrieval of the
LRS data, registration between the patient's intra-operative LRS
data and a pre-operative MR image volume were performed
retrospectively. Referring to FIG. 12, a digital photograph image
1210 of the surgical FOV with the vein of Trolard 1220 highlighted,
a corresponding textured point cloud 1230 generated
intra-operatively using the LRS are respectively shown in FIGS. 12a
and 12b.
[0100] Central to using the LRS within the clinic is to demonstrate
in vivo registration results. A clinical example of registration
results from intra-operative data is shown in FIG. 13 with
corresponding measures of registration error listed in Table 2.
Specifically, FIG. 13a shows the result 1310 of PBR-based
registration using manually localized landmarks in img and lrs.
FIG. 13b shows the result 1320 of the ICP registration using
highlighted contours 1340 in img and lrs. And FIG. 13b shows the
result 1330 of the SurfaceMI registration given the initial
alignment provided by the PBR method. The highlighted contours 1340
are prominent sulcal and vessel patterns visible in both img and
lrs spaces. For Table 2, the first, second and third columns
represent the registration methods used, the mean registration
error associated with the cortical surface points used in PBR, and
the mean closest point residual between contours, respectively.
Although PBR registration results in better fiducial error than the
ICP and SurfaceMI registrations, as shown in Table 2, the results
shown in FIG. 13 suggest that the registration error reported for
the contour points (ICP and SurfaceMI registrations) is the better
metric as to the quality of alignment than the PBR
registration.
2TABLE 2 Registration errors for in vivo alignment using PBR, ICP,
and SurfaceMI frameworks. Registration Mean Error Measure (mm) Mean
Error Measure (mm) Method Fiducial Points (n = 3) Contour Points (n
= 468) PBR 2.4 .+-. 1.0 1.9 .+-. 1.0 ICP 3.4 .+-. 1.4 0.9 .+-. 0.6
SurfaceMI 3.5 .+-. 1.7 1.3 .+-. 0.5
[0101] The results from the clinical experiment of the first
patient demonstrate the feasibility of cortical surface
registration in the OR environment as well as provide a limited
quantitative assessment to the approach's accuracy. Table 2
demonstrates that a PBR approach similar to Nakajima et al. (except
using LRS data in lieu of optical digitization) produces a mean
registration error for vessel fiducials that is 1-mm less on
average than that provided by ICP or SurfaceMl. However, in the
region of the contours, the method did not fare as well. FIG. 13
demonstrates a qualitatively better alignment in the area of the
contours when using either ICP or SurfaceMl. Table 2 also
quantifies this improved closest point residual for ICP and
SurfaceMI over the PBR method. One likely reason for this
discrepancy is that brain deformation may have occurred upon
opening the cranium and may be distributed nonuniformly over the
brain surface. This would be consistent with the results in Table 2
since the PBR method relies on the selection of the vessel
fiducials as the basis for registration while ICP and SurfaceMI
only use these for initialization. Hence, if the brain surface is
nonuniformly deformed, it would logically follow that methods which
base their registration on the vessel fiducials (PBR) would be
better within the fiducial region, while methods that use contour
information (SurfaceMI and ICP) would be better within the contour
region.
[0102] The clinical results of the first patient also demonstrate
that the registration protocol used within this work may be a
viable approach for surgeries where minimal brain shift is
encountered. In addition, the visual results shown in FIG. 13
provide new anatomical cues to surgeons by correlating the FOV
observed in the OR to the MR tomogram volume studied prior to
surgery for pre-operative planning.
[0103] The second patient was a 34-year old man with a two-year
history of paroxysmal headaches in the bitrmporal regions and a
more recent history of emotional liability. He was otherwise
asymptomatic and his past medical history was unremarkable.
Neurological examination was normal. MR imaging was performed to
evaluate persistent headaches and revealed a left inferior frontal
region tumor, which measured 2.5.times.2.0 cm. There was some
calcification within the tumor, but little enhancement with
gadolinium infusion; these findings were most consistent with a
low-grade glial neoplasm. Because of the proximity of the lesion to
the speck cortex, an awake craniotomy, with cortical mapping, was
performed, complemented by framless stereotactic guidance. The
patient was placed supine on the operating table and the head was
elevated 10.degree. and turned 60.degree. toward the right. A left
fronto-temporo-parietal craniotomy was performed and the dura
opened in a cruciate fashion to expose the frontal, anterior
temporal and enterior parietal lobes as well as the sylvian fissure
and vessels. In the anterior inferior left frontal region, an
enlarged and discolored gyrus was identified and was felt to be the
tumor by visual inspection as well as by altrasound examination and
framless stereotactic location. Mapping of Broca's ares was
performed and demonstrated that the speech area was separated from
the tumor by one gyrus. Gross total resection of the tumor was
performed. Post-operatively, he was neurologically intact.
[0104] Referring now to FIG. 14, the results of data acquisition
for the patients include a digital image 1410 of the scanning FOV,
a texture image 1420 captured at the time of range scanning, a
texture point cloud 1430 of the intra-operative surgical FOV
generated via range scanning and texture mapping, and a textured
point cloud 1440 from the pre-operative image generated via ray
casting. For the patient, the original LRS point cloud consisted of
96,407 points. Segmentation of the cortical surface from the
original cloud resulted in a point cloud density of 13,429 points.
The physical dimensions of the segmented cloud spanned a surface
area of 31.6 cm.sup.2, and were recorded at a mean distance of 26.6
cm from the origin of the scanners coordinate system. Table 3 lists
the mean TRE using the PBG, ICP and SurfaceMI registrations,
respectively, for the second patient. Comparing to the PBR and ICP
registrations, the SurfaceMI registration results in a more
accurate registration (1.95 mm MRE).
3TABLE 3 Mean TRE for the second patient. Registration Methods Mean
TRE (mm) PBR 2.86 ICP 3.18 SurfaceMI 1.95
[0105] Registration results of different methods for the data of
FIG. 14 are shown in FIG. 15, where the registration result 1510 is
generated by the cortical surface landmarkers and PBG, the
registration result 1520 is generated by the ICP transforms on the
two surfaces, and the registration result 1530 is generated by the
SurfaceMI registration. The LRS point cloud has been artificially
texture to enhance contrast. As shown in FIG. 15, the SurfaceMI
produces a better registration result than the PBR and ICP method
for the patient.
[0106] The third patient was a 47-year old woman with breast cancer
who had undergone modified radical mastectomy, radiation, and
chemotherapy two years prior to her presentation with left arm pain
and numbness and subjective left arm and leg weakness. MR
demonstrated a 2.5.times.2.5 cm right posterior frontal mass with
significant edema and mass effect, suggestive of metastatic cancer.
An awake craniotomy was conducted for the patient with frameless
stereotaxy and cortical mapping. The patient was posterior supine,
with the head elevated 5 to 10 and turned 15.degree. to 20.degree.
to left. A frontal-parietal craniotomy was performed, exposing the
midline and the vein of Trolard. The tumor was located in the
posterior right frontal region, one gyrus in front of the motor
strip. Gross total resection of a metastatic breast carcinoma was
performed. Post-operatively, the patient was neurologically
intact.
[0107] Referring now to FIG. 16, the results of data acquisition
for the third patients include a digital image 1610 of the scanning
FOV, a texture image 1620 captured at the time of range scanning, a
texture point cloud 1630 of the intra-operative surgical FOV
generated via range scanning and texture mapping, and a textured
point cloud 1640 from the pre-operative image generated via ray
casting. For this patient, the original cloud contained 96,345
points and the segmented cloud contained 11,688. The physical
dimensions of the segmented cloud spanned a surface area of 22.3
cm.sup.2, and were recorded at a mean distance of 25.7 cm. The
standard deviation in the depth measurement for patient 1 was 4.3
mm and 3.4 mm for patient 2. Table 4 lists the mean TRE using the
PBG, ICP and SurfaceMI registrations, respectively, for the third
patient. In this clinical trial, the SurfaceMI registration results
in a less accurate registration (6.11 mm MRE), comparing to the PBR
and ICP registrations.
4TABLE 4 Mean TRE for the third patient Registration Methods Mean
TRE (mm) PBR 2.55 ICP 1.91 SurfaceMI 6.11
[0108] Registration results of different methods using the data of
FIG. 16 are shown in FIG. 17, where the registration result 1710 is
generated by using the cortical surface landmarkers and PBG, the
registration result 1720 is generated by using the ICP transforms
on the two surfaces, and the registration result 1730 is generated
by the SurfaceMI registration. The LRS point cloud has been
artificially texture to enhance contrast.
[0109] For the second patient, the vessel/sulcal patterns provided
enough textural contrast for mutual information to fine-tune the
PBG/ICP alignment. While for the third patient the tumor margins
abutted the cortical surface. As such a gadolinium pattern of the
tumor attenuated the vessel/sulcal texture normally present on the
surface. The loss of the texture information resulted in the less
accurate registration.
Example 3
Tracking of Surface Deformations
[0110] Among other things, the present invention also provides a
non-rigid registration capability for the tracking of brain surface
deformations with serial range scans.
[0111] The data acquisition procedure for tracking brain surface
deformations with serial range scans is described above. A laser
range scanning device, for example, RealScan3D, was used to
intra-operatively capture a 3D topography of the surgical FOV as
well surface texture mapping to submillimeter accuracy, which
describes the deforming nature of the brain during surgery. This
scanner was mounted on a vibration-damped monopod that was brought
into and out of the surgical FOV manually. After dural opening, the
monopod and scanner were brought into the surgical FOV and the
laser scanning extents (left and right margins) were calibrated to
cover the width of the craniotomy. A laser stripe was then passed
over the brain's surface and range data was collected using the
principle of optical triangulation. After acquisition, the scanner
and monopod were moved out of the surgical FOV. For the purpose of
tracking surface deformations of a targeted region of interest, the
RealScan3D was used to sequentially scan the targeted region of
interest for acquiring a textured point cloud of the targeted of
interest at different times. A 480.times.640 pixels RGB bitmap
image registered to the range data was acquired at the time of
scanning.
[0112] Referring to FIGS. 18-20, deformations were shown for three
in vivo cases, respectively. Each in vivo case corresponded to a
surgery in a specific targeted region of interest, and was assigned
a number from C1 to C3 as the case identification. For each in vivo
case (C1, C2, or C3), as shown in FIGS. 18-20, respectively, figure
(a) was a textured point cloud (1810, 1910 or 2010) of the specific
targeted region of interest acquired early in the procedure, such
as at time t.sub.1, by the RealScan3D, figure (b) was a textured
point cloud (1820, 1920 or 2020) of the specific targeted region of
interest acquired at time t.sub.2 later than t.sub.1, by the
RealScan3D, figure (c) a result (1830, 1930 or 2030) of a rigid
body registration of the textured point cloud (1810, 1910 or 2010)
of figure (a) to the textured point cloud (1820, 1920 or 2020) of
figure (b), and figure (d) was a result (1840, 1940 or 2040) of
both a rigid body registration and nonrigid registration of the
textured point cloud (1810, 1910 or 2010) of figure (a) to the
textured point cloud (1820, 1920 or 2020) of figure (b). As shown
in FIGS. 18-20, serial intra-operative images are very different
from each other because of large resections (other factors include
the appearance and/or disappearance of surgical instruments within
the surgical FOV). This presents particular challenges to intensity
based registration algorithm. In this exemplary embodiment of the
present invention, it was necessary to outline manually targeted
region of interest to specify regions over which the
transformations was computed. The dashed lines shown in FIGS. 18a,
18b, 19a, 19b, 20a and 20b defined these targeted regions of
interest. Homologous landmarks indicated by number 1 to 7 in FIGS.
18a, 18b, 19a, 19b, 20a and 20b and corresponding contours in all
these figures were selected and used for quantitative evaluation of
the registration results.
[0113] Quantitative evaluation was performed as follows. The
deformation field .xi.(x.sub.i) was used to project the point
x.sub.i onto the deformed image to find the deformed points,
x.sub.i'=.xi.(x.sub.i). (3)
[0114] The error for each pair of points (.epsilon..sub.i) was
computed as the Euclidian distance between the manually selected
points y.sub.i on the deformed image and the corresponding
transformed points x.sub.i' as follows,
.epsilon..sub.i=.parallel.y.sub.i-.xi.(x.sub.i).parallel.. (4)
[0115] Tables 5-7 present the quantitative results for the in vivo
cases C.sub.1-C.sub.3, respectively. In each of these tables,
d.sub.in refers to the registration error prior to registration,
.epsilon..sub.r is the registration error after rigid body
registration, and .epsilon..sub.nr is the registration error after
both rigid and nonrigid registration. The large error prior to
rigid body registration is due to the fact that the scanner was not
placed at the same position for the first and second image
acquisition.
5TABLE 5 Registration error for the in vivo case C1, d.sub.in,
prior to registration, .di-elect cons..sub.r, after rigid body
registration, and .di-elect cons..sub.nr after nonrigid
registration. Landmarks d.sub.in [pixels] .di-elect cons..sub.r
[pixels] .di-elect cons..sub.nr [pixels] 1 16, 13 6, 83 0, 38 2 33,
54 6, 93 0, 22 3 19, 31 7, 15 0, 25 4 14, 21 8, 51 0, 34 5 17, 46
9, 99 0, 50 6 25, 55 5, 02 0, 54 7 36, 77 0, 11 0, 30 Mean .+-. SD
23.28 .+-. 8.90 6.36 .+-. 3.16 0.36 .+-. 0.12
[0116]
6TABLE 6 Registration error for the first in vivo case C2,
d.sub.in, prior to registration, .di-elect cons..sub.r, after rigid
body registration, and .di-elect cons..sub.nr after nonrigid
registration. Landmarks d.sub.in [pixels] .di-elect cons..sub.r
[pixels] .di-elect cons..sub.nr [pixels] 1 66, 29 9, 10 0, 40 2 65,
80 10, 23 0, 32 3 64, 82 11, 79 0, 31 4 62, 80 13, 32 2, 25 5 61,
22 12, 08 0, 65 6 59, 67 11, 87 0, 51 7 56, 22 12, 27 0, 25 Mean
.+-. SD 62.40 .+-. 3.64 11.52 .+-. 1.40 0.67 .+-. 0.71
[0117]
7TABLE 7 Registration error for the first in vivo case C3,
d.sub.in, prior to registration, .di-elect cons..sub.r, after rigid
body registration, and .di-elect cons..sub.nr after nonrigid
registration. Landmarks d.sub.in [pixels] .di-elect cons..sub.r
[pixels] .di-elect cons..sub.nr [pixels] 1 38, 60 2, 24 0, 11 2 39,
29 1, 00 0, 51 3 40, 52 1, 00 0, 44 4 42, 72 2, 82 0, 53 5 40, 52
1, 00 2, 18 6 41, 98 2, 24 0, 24 7 39, 56 1, 41 2, 20 Mean .+-. SD
40.46 .+-. 1.47 1.67 .+-. 0.75 0.89 .+-. 0.90
[0118] The results shown in FIGS. 18-20 and Table 5-7 indicate that
automatic intra-operative tracking of brain motion using a LRS is
feasible. Despite large differences in the images due to resection
and different viewing angles the approach of the present invention
is robust enough to lead to subpixel registration errors.
[0119] Further Discussions
[0120] In the present invention, among other things, a fast,
systematic, non-invasive, non-contact method for registering
pre-operative images to the intra-operative patient's cortical
surface and for measuring the extent of brain shift during surgery
is disclosed. The method, in one embodiment, aligns
patient-to-image and tracks the brain for use as input for
model-based brain shift compensation strategies. This represents a
fundamental advancement for facilitating the possibility of using
low-cost computational models to compensate for brain shift during
image-guided surgery.
[0121] Another important aspect of the SurfaceMI of the present
invention is its ability to perform multimodal registration. Within
the phantom and clinical experiments, SurfaceMl represents a
multimodal registration between CT data and CCD color texture, and
MR data and CCD color texture, respectively. This result is quite
remarkable and adds impetus for the use of laser-range scanning
within the neurosurgical OR environment.
[0122] The method according to one embodiment of the present
invention in conjunction with the quantitative results provide
substantial motivation for using LRS technology within the
neurosurgical OR. LRS methods provide rapid detailed
characterization of the cortical surface during surgery and can be
used as a tool for registration and the eventual measurement of
deformation. This versatility will make LRS technology advantageous
in pursuing model-updating strategies for the compensation of brain
shift during image-guided neurosurgery.
[0123] More specifically, phantom experiments are presented that
compare traditional point-based and surface-based (ICP)
registration methods to a novel registration approach which uses a
combined geometric and intensity-based metric (SurfaceMI). The
registration approach is a 3D surface alignment technique that
begins with an ICP-based initialization followed by a constrained
mutual information-based refinement. The algorithm has demonstrated
better accuracy with respect to deep tissue targets within the
simulated craniotomy region. However, some limitations did appear
within the robustness studies whereby a 2% failure rate occurred
during phantom registration experiments and clinical trials. In
this example shown in FIG. 11, ICP resulted in a better FRE on
average with tighter standard deviation than SurfaceMI. The
SurfaceMI had produced eight outliers over 500 trials. The areas of
local extrema were found near the global extrema and resulted in
frustrating numerical optimization methods. These outliers
represent a less than 2% failure rate. Furthermore, if the outliers
are eliminated from the trial set, the FRE is sharply reduced from
mean error of 3.4-2.2 mm. Alternative optimization and
multi-resolution methods need to be investigated further to
decrease this failure rate [39-41].
[0124] One the other hand, for the in vivo cases of the tracking of
surface deformations, the algorithm still requires manual
intervention to delineate targeted regions of interests over which
the transformations are computed but these targeted regions of
interests do not need to be delineated very carefully. Further
development will address this issue.
[0125] While there has been shown several and alternate embodiments
of the present invention, it is to be understood that certain
changes can be made as would be known to one skilled in the art
without departing from the underlying scope of the invention as is
discussed and set forth above and below. Furthermore, the
embodiments described above are only intended to illustrate the
principles of the present invention and are not intended to limit
the scope of the invention to the disclosed elements.
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